This tutorial is based partially on the tutorial: http://rstudio.github.io/leaflet/choropleths.html

Load our interesting Data

We will be working with a dataset from the United States Census Bureau of National Population Totals and Components of Change: 2010-2017. This dataset contains the estimated population of each state every year from 2010 through 2017. We will be visualizing the percent change in population for each state in 2016 vs 2010.

Download the data, load into R, munge

# load in the csv
pop.estimates <- read.csv("nst-est2017-popchg2010_2017.csv", stringsAsFactors = FALSE)

# create a column of the population growth in 2017 versus 2010
pop.estimates$difference <- pop.estimates$POPESTIMATE2017 - pop.estimates$POPESTIMATE2010

pop.estimates$percentagegrowth <- pop.estimates$POPESTIMATE2017 / pop.estimates$POPESTIMATE2010

# drop the information for overall United States and Regions (we only want states)
pop.estimates <- pop.estimates %>% 
  filter(STATE != 0)
## Warning: package 'bindrcpp' was built under R version 3.4.4

Grab a GeoJSON file

With our data loaded, we are ready to begin with the visualization. We’ll start by finding and loading the GeoJSON information from a JSON file.

# download the .json and save it
# u <- "enter url here"
# downloader::download(url = u, destfile="us-states.geojson")

We’ll use the geojsonio package to load the data into sp objects. The sp package provides classes and methods for dealing with spatial data in R which will let us easily manipulate the geographic features, and their properties.

# use geojsonio to load the spatial data into sp objects
states <- geojsonio::geojson_read("gz_2010_us_040_00_500k.json", what= "sp")

Take a look at the data structure of this object.

Combining our data with the SpatialPolygonsDataFrame

Let’s take a look at the order of the states in the two data frames. What do you notice?

# SpatialPolygonsDataFrame(states@data)

WARNING: There is much misleading information online about how to merge a dataset with your SpatialPolygonsDataFrame. Be careful! The states@data data frame needs to be in the same order as the JSON data. R will happily merge dataframes in a new order, leading to plotting of the wrong data for each state!

Hint: Look at the options in merge.

# Add a new column to the SpatialPolygonsDataFrame@data with our data of interest
states@data <- base::merge(states@data, pop.estimates %>% 
                             select(NAME, percentagegrowth)
                        , by="NAME", sort=FALSE)

# states@data <- merge(states@data, pop.change %>% select(NAME, percentagegrowth), by = "NAME", sort = FALSE)

# head(states.df)

Our First Leaflet

Let’s start out by visualing the polygons described in our SpatialPolygonsDataFrame.

# provide leaflet with the SpatialPolygonsDataFrame
# set the view to the contiguous United States
# set what the background map should look like.
    #addTiles() # basic
    #addProviderTiles("Stamen.Watercolor") #FUN
# why is -96 negative? because we're west of the Prime Meridian
# what zoom would you use to look at a city?

m <- leaflet(states) %>% 
  setView(-96, 37.8, 4) %>% # set long, lat, zoom-in by factor of 4
  addProviderTiles("Stamen.Watercolor")
m

Almost beautiful enough to stop there. But let’s add the polygons described in our SpatialPolygonsDataFrame.

m %>% addPolygons()

It seems like we just ruined a perfectly good watercolor. This needs some data to redeem the map.

Color by some feature in the SpatialPolygonsDataFrame.

We now want to color by a feature of our data, the percentage of growth from 2010 to 2017 in each state. First, we need to create our color scale for this data. Let’s split bin on populations that have decreased and increased

We will now create bins based on this range and use those bins to divide a colorscale up.

Use a histogram & look at the distribution of

hist(states@data$percentagegrowth)

bins <- seq(0.85,1.15, 0.05)
# pal <- colorBin("YlOrRd", domain = "column to color by", bins = bins)
pal <- colorBin("Blues", domain = "column to color by", bins = bins)

Now, using the feature data we will color the polygons and add a nice border.

withcolor <- m %>% 
  addPolygons(
    fillColor = ~pal(states$percentagegrowth),
    weight = 2,
    opacity = 0.5,
    color = "white",
    dashArray = "3", # make the borders dashes
    fillOpacity = 0.7
            )
withcolor

It’s a choropleth. But wait! What do all those colors mean?

Add a legend

withLegend<- withcolor %>% 
  addLegend(
    pal = pal,
    values = ~states$percentagegrowth,
    opacity = 0.7,
    title = "Population Growth Since 2010",
    position = "bottomright"
            )
withLegend

Better as far as responsible reporting goes. We can quickly see which states had a population decrease in 2017 from 2010. However, this seems to be a waste of the visual space. We could have simply listed states that saw a decrease in population and not used up so much of the page. Let’s make this map more informative. It would be interesting to see differences in the percent increase, 15% population increase in 7 years is quite different than 0.08% increase.

Lab Exercise 1: Play with the binning to make the map more informative.

Lab Exercise 2: Aesthetics: Improve the legend, change the color scheme.

Advanced: Find a different provider tile for the background and change the aesthetics to match

Adding Interactivity

Now what this map needs is some interactivity. It’s 2018, you can’t have a visualization without it.

First, we’re going to create a response to hovering over the polygons.

labels <- 

hovering <-m %>% 
  addPolygons(
    fillColor = ~pal(states$percentagegrowth),
    weight = 2,
    opacity = 0.5,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE
    )
    

)
hovering  # what about the legend?

Lab Exercise 3: Wow that hover border is gross looking. Please fix it

Finally, we are going to create a popup to provide information while hovering.

# labels <- 
# 
# final <- m %>% 
#   addPolygons(
#   # Add code here
# )
# final %>% # what's missing again?

Note* Formating the labelOptions doesn’t work for me.

And we’ve done it! An interactive choropleth!

Now, does it make sense to use the percent increase? Will we see anything different if we use raw numbers? We originally created a column for the difference in population from 2017 to 2010.

Lab Exercise 4: Swap the data to the raw difference in population

Lab Exercise 5: If you haven’t already, change the aesthetics of the map

Advanced: Find a dataset at the county level (optional: of Florida). Find a geoJSON with county level information. Use Leaflet to create an interactive map.

Challenge: Feeling like your map is looking pretty good? Enter your map to be evaluated by your peers at the end of class for a chance to win a prize.